From employee socialization to co‐evolution: A lifespan multidisciplinary conceptualization
Bibliographic record
Abstract
Abstract Various theories have highlighted how employees evolve in their organization and how organizations influence this process, but only portray part of the complex relations among these interacting social entities. We thus propose a meta‐theory to unify these multiple theories, including symbolic interactionism, employee/organizational socialization theory, human resource management (HRM) systems theory, cultural consensus theory, and self‐determination theory. This integration seeks to increase our understanding of the co‐evolution process unfolding over time between individuals and the organizations to which they belong. We first propose a multilevel expansion of the symbolic interactionist framework typically used to described employee socialization. In doing so, we integrate organizational culture, climate, identity, image, reputation, and HRM systems as distinct meso‐social phenomena that can be simultaneously considered in the co‐evolution process and themselves be influenced by macro‐social processes. We then outline how this proposed framework can explain the dynamic co‐evolution occurring between employees and the organization, hoping to spur research on the improvement of social entities through psychological means.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".